Top Data Jobs in Toronto

Lead data architecture for a Justice Information Management System modernization: consolidate reporting marts into an EDW, perform data profiling and modeling, develop ETL (SSIS), ensure data quality and governance, validate reporting, and communicate with stakeholders.

Design and define enterprise data architectures and logical/physical data models using Kimball dimensional modelling; develop models with PowerDesigner; work with JOIN and JIMS; ensure alignment with information management, cybersecurity, privacy, and data governance; communicate technical concepts to technical and business stakeholders; support modernization initiatives.

Lead product design for Stripe's Data & AI products, shaping systems and new products from ambiguous strategic problems to detailed interaction design. Collaborate cross-functionally with PMs, engineers, and design leaders to build trust-focused, end-to-end experiences and design systems.

Lead product analytics for usage, activation, adoption, retention, and expansion. Define metrics, improve instrumentation, build reusable measurement frameworks, apply AI-first analytics workflows, design experiments and observational analyses, create monitoring/alerting, surface insights to influence product decisions, and mentor analysts.

Lead presales engagements to design enterprise data platforms and governance on Microsoft Fabric and Azure Databricks. Conduct discovery workshops, architect governance with Purview/Unity Catalog, define AI-readiness and Copilot access, author proposals/RFP responses, run POCs/demos, and partner with sales and delivery teams to size and transition engagements to managed services.

Lead product vision and roadmap for Imogen Data, a platform that captures and transforms legacy system data. Collaborate with engineering, sales, marketing, and customer-facing teams to prioritize investments, drive execution, mitigate risk, and represent the product to stakeholders and customers. Evolve the product from MVP to enterprise scale while balancing strategy, delivery, and customer value.

Use diverse data sources to evaluate product performance, build automated ETL/data workflows, design dashboards and KPIs, own data product quality, and provide analytical insights to guide product and business decisions while establishing monitoring frameworks and best practices.

Design, build, and maintain scalable data systems and ETLs on GCP; connect production data to business systems; generate analytics and ML-driven insights for GTM teams; work cross-functionally and introduce modern tools (Airflow, DBT, Presto, Hightouch).

Provide clinical data analysis, audits, and workflow recommendations to ensure compliance (HEDIS, URAC, NCQA). Design data pulls, produce analytical and audit reports, support corrective action plans, training, and regulatory audit preparation. Collaborate with clinical teams and data analysts to track performance, forecast staffing, and improve operational processes.

Design, develop, and deploy machine learning and NLP solutions at scale. Work with cross-functional teams to build content classification, representation learning, search, and recommendation models using classical ML, deep learning, and LLMs. Process large datasets with Python, SQL, and Spark, and communicate results to stakeholders with clear documentation.

Design, build, and maintain high-performance data pipelines on Snowflake using dbt and AWS. Own code (SQL, Python, Shell, Terraform), ensure data quality and governance, troubleshoot complex issues, lead agile delivery and mentor engineers, collaborate with architects and QA, and produce technical documentation.

Review sports video to identify sport type, player actions, tactical movements, and game events. Apply predefined taxonomies, record timestamps, ensure annotation consistency, follow guidelines, and participate in quality calibration and testing to train AI systems.

Plan and execute a zero-downtime migration of stateful data systems (Druid, Spark, Redpanda, Airflow, PostgreSQL, Elasticsearch) from bare-metal VMs to Kubernetes on OpenStack. Codify infrastructure as code, implement safe StatefulSet/PVC and upgrade patterns, and own day-to-day operational health. Build observability, SLOs, runbooks, KPIs, and optimizations. Partner with SRE on hardware, networking, and Kubernetes fundamentals while owning data applications end-to-end.

Design, develop, test, and support data and AI products. Partner with stakeholders to translate requirements, build ETL/ELT pipelines, perform data modeling/profiling, automate frameworks, troubleshoot production issues, and participate in process improvements.

Lead design of scalable, governed end-to-end data solutions aligned to business priorities. Partner with product, engineering and architecture teams to define target-state patterns, integration, storage, consumption, controls and governance for reporting, operational and AI use cases. Provide technical leadership, review solutions, and ensure alignment with enterprise standards and platform strategy.

Design and lead enterprise data architecture for agentic AI, including hybrid lakehouse and graph/vector integrations. Define schemas and knowledge graphs, ensure database performance and governance across Snowflake/Kinetica/BigQuery, establish ETL/ELT and CDC/streaming strategies, and act as technical client lead for architecture, security, and performance benchmarks.

Build and maintain production-grade data processing systems and foundational infrastructure for enterprise AI. Collaborate with researchers and engineers, deliver end-to-end implementations, transform unstructured data into performant datasets, and use distributed processing frameworks and modern analytics tooling to inform products and strategy.

Lead design and implementation of scalable, secure Forma Data cloud services. Act as technical lead across full software lifecycle, write and operate cloud services, mentor engineers, solve complex distributed-system problems, and collaborate globally to integrate SaaS products with Autodesk tools.

Develop data-analysis pipelines bridging ultrasound, synthetic biology, imaging and sequencing. Collaborate with wet-lab and ML teams to define objectives, encode priors, run ML pipelines for de-novo design and closed-loop active learning, and produce actionable insights to accelerate molecule discovery and device optimization.

Build and maintain financial and operational datasets and Power BI dashboards; map metrics across eCommerce and engineering sources; support AOP, ARR, pricing and retention modeling; run CI/CD pre-production checks, monitor processes, and partner cross-functionally with Finance, Marketing/Retention, and Product.

Lead architecture and implementation of agentic AI systems powering Zuma's platform. Rebuild onboarding and integration frameworks, implement analytics and observability for LLM performance, translate product needs into technical solutions, mentor engineers, establish engineering standards, and work directly with customers to enable scalable, production-grade AI experiences.

Perform L3 network troubleshooting and RCA on production enterprise networks, review telemetry/logs/configs, label and annotate network behaviors for ML training, and help define schemas and data pipelines to support AI-driven infrastructure.

The AI-Native Data Engineer will build data pipelines, architect databases, design GCP infrastructure, and automate workflows for energy management using AI.

Lead enterprise data, analytics, and AI transformation engagements as a strategic advisor and architect. Design cloud data platforms, warehouses/lakehouses, governance, BI/semantic models, and integration strategies. Provide delivery oversight, mentor teams, conduct architecture reviews, and support pre-sales and thought leadership to drive measurable business outcomes.

Review and analyze news content from patient advocacy groups, extract insights, and input data into Zebricks to support AI/ML processes. Collaborate with the team on data projects, shape data best practices, and perform web research and spreadsheet-based data work.